Insightful and accurate data is the lifeblood of any successful business. Without it, you may find yourself missing out on opportunities or dissatisfaction from your customers— not what we want at all! There are many ways to ensure high-quality information in Salesforce Sales Cloud with regular cleansing routines that keep up-to date records for each client as well ensuring uniformity across departments who handle this type of work independently but still require guidance when needed most so they can do their job effectively too.
Maintaining good records will make sure you have reliable, accurate information for your company. First of all, it’s important to ensure accuracy in the first place since there can never be any errors once they’re entered into Salesforce! You should also regularly clean and update this info so as to not have duplicate records or inaccurate details. Data quality is important in sales because it allows the sales team to:
- Forecast accurately – Inaccurate data can lead to inaccurate forecasting. This can cause problems when trying to budget or plan for future growth.
- Make informed decisions – Bad data can distort your view of the customer landscape or specific opportunities, leading you to make poor decisions about targeting and strategies.
- Missed opportunities – If you don’t have complete and accurate information about your customers, you may miss out on important sales opportunities.
- Lower customer satisfaction levels – If your data is inaccurate, it can lead to frustration and dissatisfaction on the part of your customers.
In our estimate, at least 5% of the Salesforce Sales Cloud data in every organization is inaccurate at any given time— resulting in more than $60 billion in lost productivity and missed opportunities each year. As you can see, data quality is essential in sales and customer management. Let’s first look at the main areas that you need to focus.
Salesforce Sales Cloud Structure
This is Salesforce Sales Cloud simplified. Here is a pictorial view of Sales cloud’s object relationship that I have used as the best practice diagram:
Your key objects are the following:
- Lead – Leads are created manually or comes through your marketing channels. This is the starting point of your sales funnel. Data completeness and accuracy at this stage is important as you would want your pipeline to be clean.
- Contact – Leads get converted to a contact. This can happen when you are converting a lead to accounts or can be independently converted. Duplicate contacts will become a nightmare for the Sales or Account reps. There can be multiple contacts associated with an account.
- Account – Accounts are created when a lead is converted into an account. Accounts can have lot of duplicates if your process determines that every BU of your customer should be created as a separate account. But all accounts should have at least 1 contact associated with it otherwise it becomes as orphan account. This account information can be connected to your Finance and/or Supply Chain systems. Relevancy of completed and accurate account information becomes more critical in such scenarios.
- Opportunity – When business projects or sales are identified, those are treated as opportunities. Every opportunity should be associated with an account. An account can have multiple opportunities associated with them. Your opportunity maturity is managed through stages. You can define your own stages, but it is important to adhere to those stages during data entry or updates in opportunities. Your sales reporting will be pulled from your Opportunities and hence the importance of keep it always up to date is critical.
- Quote – Quote is typically an optional object. In some situation, this is not used. When used, there could be multiple quotes created for an opportunity. Quote also required direct feed of products and pricing that could be coming from other ERP systems.
If you keep your data quality high on these objects, you will see significant value in your sales and other upstream and downstream systems.
Why Do We Have Data Quality Issue in Salesforce?
There are several reasons why data quality issues can arise in Salesforce. One common cause is poor data governance. This refers to the process by which organizations manage and maintain their data. Without proper governance, data can quickly become outdated, inaccurate, and incomplete. For example, users might be typing the name of the cities, states and countries that leads to inconsistencies due to typos and fat fingering.
Another reason for data quality issues is the lack of training. Sales team may not be aware of how to properly input or update information in Salesforce. This can lead to incorrect or missing data. Simple mistakes include accounts with no contact association; stages might have been created properly but not managed at every opportunity level; phone number field having other information included and more.
Finally, some businesses simply don’t prioritize data quality. They may not see the value in taking the time to ensure that their sales data is accurate and up to date. However, this can have serious consequences down the road. For a large organization, someone decided to create a new country called Italia (even though the country Italy existed) in Salesforce and started to enter all information using the new country. The downstream consequences slipped their minds. This created a financial reporting nightmare when finance could not balance their numbers— all because of very small data quality issues that was not monitored as a priority for this organization.
How Do We Ensure Higher Quality of Data in Salesforce?
In order to ensure that your data is of high quality, there are a few things you can do:
- Know your data – Discover all the anomalies in your data. This would avoid surprises and help you create and remediation strategy. Today, tools can quickly perform data health check assessments for you instead of trying to review your data manually.
- Invest in data cleansing – Data cleansing is the process of correcting known errors in your data. This can be done manually or using automated tools.
- Regularly update your data – It’s important to keep it up to date to reflect the current state of your customer relationships accurately. You should make it a habit to review and update your Salesforce data regularly.
- Ensure complete and accurate data – Make sure that all the data you input into Salesforce is complete and accurate. This includes customer contact information, account details, opportunity information, etc.
Proactive vs. Reactive
In theory, the above steps can help a customer address a data quality issue. However, these approaches are after-the-fact and could be costly to implement. A better solution is to focus on continuous and intelligent data quality checks. This is a proactive approach that can help you avoid data quality issues before they arise. There are a few ways to do this, but some common methods include:
- Data profiling – The process of analyzing your data to identify patterns and trends. This can help you identify potential problems so that you can take steps to prevent them.
- Automated data quality checks – This involves using software to identify and correct errors in your data automatically. This includes things like duplicate records, incorrect field values, and missing data.
- Automated Data cleansing – As we mentioned above, data cleansing is the process of identifying and correcting errors in your data. However, it can also be used as a preventive measure to ensure that your data is clean and accurate from the start.
By taking a proactive approach to data quality, you can avoid the costly and time-consuming process of cleaning up your data after the fact.
Selecting the Right Data Quality Tool
There are a number of different data quality software solutions in the market. You should consider your specific needs and requirements to find the right one for your business. Some things to keep in mind include:
- Integration – The solution should integrate with Salesforce so that you can easily update and manage your data.
- Ease of use – The solution should be easy to use so that you can get up and running quickly.
- Accuracy – The software should be able to identify and correct errors in your data so that it is always accurate and up-to-date. Software should be able to use advanced logic to find duplicates, including fuzzy duplicates, one to many relationships.
- Automation – Look for a solution that offers automatic error correction so that you can avoid manual data entry.
Finding the right data quality software solution can save you a lot of time and money in the long run. Implementing a proactive approach to data quality is the best way to ensure that your Salesforce data is accurate and up-to-date. Don’t wait until there’s a problem— take action now to prevent problems down the road. Review RoutineAI as a prospective tool.
Let me know if there are any questions in the comments below! Happy selling!